Modeling the Mixed-Service Fleet Problem of Shared-Use Autonomous Mobility Systems for On-Demand Ridesourcing and Carsharing With Reservations

Hoseb Abkarian, Hani S. Mahmassani*, Michael Hyland

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Scopus citations

Abstract

This paper studies a mixed-service operation of shared-use autonomous mobility systems (SAMS) where customers can request rides either immediately or through reservations and use the vehicle for a point-to-point service or a time-slot-based rental service, respectively. Three autonomous-vehicle-to-user assignment strategies are presented: a first-come-first-served strategy and two optimization-based (bipartite matching) strategies. The mathematical formulations attempt to achieve a good trade-off between the wait times of reservation-based users and on-demand users, while minimizing overall empty fleet miles. A case study in Chicago is presented using taxi data, and the combined mixed-service fleet operation is compared with a case with two separate operations: one for an on-demand point-to-point service and one for a reservation-based time-slot rental service. Results show that a combined mixed-service operation can provide a more balanced service than the case with two separate operations with respect to the key performance measures of wait time and empty fleet miles.

Original languageEnglish (US)
Title of host publicationTransportation Research Record
PublisherSAGE Publications Ltd
Pages363-375
Number of pages13
Volume2676
Edition8
DOIs
StatePublished - Aug 2022

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is partially funded through a collaborative project with King Abdulaziz University of Science and Technology for joint work on ‘‘Autonomous Service Planning, Design and Real Time Operation.’’ Additional funding was provided through the Northwestern University Transportation Center.

Keywords

  • algorithms
  • autonomous
  • innovative public transportation services and technologies
  • matching
  • mathematical modeling
  • mobility as a service (MAAS)
  • networks
  • operations
  • optimization
  • planning and analysis
  • public transportation
  • ride hailing—ridesharing
  • routing
  • self-driving
  • services
  • shared
  • simulation modeling
  • systems modeling
  • transportation network companies (TNC)
  • transportation supply
  • vehicle-highway automation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

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